import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori,association_rules
import matplotlib.pyplot as plt
import networkx as nx

df = pd.read_excel('associationRules/餐厅数据.xlsx')

#print(df.head)
trsations = df['菜品'].str.split(',').to_list()

ts=TransactionEncoder()
te_ary=ts.fit(trsations).transform(trsations)
df_enecoded=pd.DataFrame(te_ary,columns=ts.columns_)

#print(df_enecoded.head)
frequent_itemsets=apriori(df_enecoded,min_support=0.1,use_colnames=True)
frequent_itemsets.sort_values(by='support',ascending=False,inplace=True)

#print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x : len(x) == 2)])

rules=association_rules(frequent_itemsets,metric='confidence',min_threshold=0.1)

effective = rules[
    (rules['lift'] > 1) & (rules['confidence'] > 0.1)
].sort_values(by=['lift','confidence'],ascending=False)

print(effective[['antecedents','consequents','support','confidence','lift']])

plt.rcParams['font.sans-serif'] = ['Simhei']
plt.rcParams['axes.unicode_minus'] = False

G=nx.DiGraph()
for _, row in effective.iterrows():
    G.add_edge(','.join(list(row['antecedents'])),
               ','.join(list(row['consequents'])),
               weight=row['lift']
               )
plt.figure(figsize=(12,8))
pos = nx.spring_layout(G)
nx.draw(G,pos,
        with_labels=True,
        edge_color=[G[u][v]['weight'] for u,v in G.edges()],
        width=2.0,
        edge_cmap=plt.cm.Blues)
plt.show()